文章摘要
姚建丽,胡红萍,白艳萍,王建中,李薇.基于GAPSO-MUSIC算法的矢量水听器的DOA估计[J].西南民族大学自然科学版,2019,45(4):383-389
基于GAPSO-MUSIC算法的矢量水听器的DOA估计
DOA estimation of vector hydrophone based on GAPSO-MUSIC algorithm
投稿时间:2018-12-06  修订日期:2019-05-29
中文关键词: 粒子群算法  遗传算法  矢量水听器  多重信号分类算法  波达方向角
英文关键词: particle swarm optimization  genetic algorithm  vector hydrophone  multiple signal classification algorithm  direction of arrival
基金项目:国家自然科学基金资助项目 ( 61774137 ) 、山西省自然科学基金资助项目 ( 201701D121012, 201701D221121)和山西省回国留学人员科研项目(2016-088)
作者单位E-mail
姚建丽 中北大学 471379547@qq.com 
胡红萍 中北大学  
白艳萍 中北大学  
王建中 中北大学  
李薇 中北大学  
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中文摘要:
      针对传统的MUSIC算法存在需要多维的非线性搜索、计算量大等问题,提出了混合粒子群遗传算法和MUSIC算法相结合的方法,对矢量水听器的波达方向(Direction of Arrival,DOA)更好的进行估计。该算法利用遗传算法的交叉算子和变异算子避免了粒子群算法早熟且易陷入局部最优,同时利用群优化算法搜索能力强的优势,更好的对波达方向进行估计。仿真实验和湖试实验表明,混合遗传粒子群算法与MUSIC算法相结合对DOA估计具有更好的性能,精度更高,具有很好的实用性。
英文摘要:
      Aimed at the problems existing in the traditional MUSIC algorithm, multi-dimensional nonlinear search is needed, and there are certain difficulties in the amount of calculation. In order to better estimate the Direction of Arrival (DOA) of vector hydrophones, a hybrid particle swarm genetic algorithm and MUSIC algorithm are proposed. This is the crossover operator, and mutation operator of genetic algorithm can improve the disadvantage that the particle swarm algorithm is easy to premature and easy to fall into local optimum. The search ability is improved by using the group optimization algorithm. Simulation experiments show that the hybrid genetic particle swarm optimization algorithm combined with the MUSIC algorithm has better performance and higher accuracy for DOA estimation. The lake experiment also shows that the algorithm has a higher estimation performance and has better practicability.
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